One of social media monitoring's benefits is that the results reveal trends in opinions that are not uncovered by ongoing customer satisfaction survey research. Recently, many companies have moved toward mining social media data for customer opinions and in parallel, reduce the amount of time and money spent on customer satisfaction survey research. This approach unfortunately may reduce the insight a company has on customer satisfaction. This presentation will explore the problems with only mining social media data, and will demonstrate the benefits of combining survey research with social media monitoring. We will show how to properly perform the the data integration and then classify the combined results to attain the holy grail of all customer satisfaction research: find out negative sentiment about the company that wasn't previously known and, if left unexplored, will impact the bottom line.

Sophisticated trading and investment firms, seeking a competitive edge from managing 'big data,' need to make sense of all the unstructured news and opinion distributed every day over, not only traditional news sources, but financial news sites, chat rooms, blogs and other social media channels as well. With modern economic and financial theory increasingly looking towards behavioural and crowd-based models to explain cyclical booms and busts, the ability to quantitatively assess broad market sentiment can be a valuable indicator to help guide successful trading and investment strategies. The world of 'big data' is a new market data frontier that can provide trading firms with a real edge by helping to model behavioural economics. News and Social Media Sentiment tools

allow traders to quantify market sentiment from the millions of public and premium sources of financial information available via the Web, turning a lot of noise into actionable insights.

There are three forms of power: knowledge, position and influence. In the social space, position is irrelevant. Knowledge and influence is paramount. How that comes across is captured in sentiment. Sentiment is defined as thoughts, views, or attitudes, especially ones based mainly on emotion instead of reason. Sentiment drives a person' s consideration set and thereby influences their decisions. Too often people focus on the 'poles', e.g. the positive or negative sentiment and miss the opportunity capture by neutral sentiment, aka the 'swing voter'. Taking the time to consider this swing sentiment will in turn shape strategies, products, offers and messaging.

As social web becomes increasingly mainstream as a customer engagement channel, it grabs a larger share of interest and intrigue from customer experience practitioners and sentiment analysis technologist. Banafsheh Ghassemi will discuss practical opportunities and blind spots this trend delivers in a multi-channel world.

Researchers and practitioners are developing and applying new methods to determine how Web users feel about everything: products and politicians, medical drugs, friends and family, scientific articles and celebrities. In this talk we will cover recent advances that combine information extractionwith sentiment analysis to improve accuracy in assessing sentiment about specific entities. We will present several real world applications ofsentiment analysis that utilize unsupervised learning and semantic clustering.

Sentiment analysis exploits academic and industrial research to enable business solutions that harvest and make use of opinion, attitude, and emotion in the broad array of human communications. Progress is constant, with much room for further advances: Subject matter for what should prove to be an illuminating discussion of sentiment analysis Innovation, featuring panelists who are themselves exemplary Innovators.